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LPCNet
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dump_lpcnet generates constant size sparse arrays

pull/10/head
David 2019-08-02 12:23:58 +09:30
parent cff1d4a888
commit ab81ee805a
2 changed files with 49 additions and 30 deletions
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49
src/dump_lpcnet.py
View File

@ -43,8 +43,8 @@ max_mdense_tmp = 1
def printVector(f, vector, name, dtype='float'):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
#print('static float ', name, '[', len(v), '] = \n', file=f)
f.write('static {} {}[{}] = {{\n '.format(dtype, name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(v[i]))
if (i!=len(v)-1):
@ -78,9 +78,28 @@ def printSparseVector(f, A, name):
idx = np.append(idx, j)
W = np.concatenate([W, A[j, i*16:(i+1)*16]])
idx[pos] = nb_nonzero
printVector(f, W, name)
#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
# set a maximum length to accomodate run time loading of other
# networks that may not have been sparsified, or sparsified to
# different lengths
print(A.shape[0]*A.shape[1])
print(W.shape)
print(idx.shape)
max_W = A.shape[0]*A.shape[1]
sz = max_W - W.shape[0]
zeropad = np.zeros((sz,), dtype='int')
W = np.concatenate((W,zeropad))
max_idx = 32767
sz = 32767 - idx.shape[0]
zeropad = np.zeros((sz,), dtype='int')
idx = np.concatenate((idx,zeropad))
printVector(f, W, name)
printVector(f, idx, name + '_idx', dtype='int')
return;
def dump_layer_ignore(self, f, hf):
@ -105,11 +124,11 @@ def dump_sparse_gru(self, f, hf):
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = max(max_rnn_neurons, neurons)
f.write('const SparseGRULayer {} = {{\n {}_bias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
f.write('SparseGRULayer {} = {{\n {}_bias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const SparseGRULayer {};\n\n'.format(name));
hf.write('extern SparseGRULayer {};\n\n'.format(name));
return True
def dump_gru_layer(self, f, hf):
@ -130,11 +149,11 @@ def dump_gru_layer(self, f, hf):
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = max(max_rnn_neurons, neurons)
f.write('const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
f.write('GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const GRULayer {};\n\n'.format(name));
hf.write('extern GRULayer {};\n\n'.format(name));
return True
CuDNNGRU.dump_layer = dump_gru_layer
GRU.dump_layer = dump_gru_layer
@ -142,10 +161,10 @@ GRU.dump_layer = dump_gru_layer
def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
printVector(f, weights, name + '_weights')
printVector(f, bias, name + '_bias')
f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
f.write('DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights.shape[0], weights.shape[1], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
hf.write('extern const DenseLayer {};\n\n'.format(name));
hf.write('extern DenseLayer {};\n\n'.format(name));
def dump_dense_layer(self, f, hf):
name = self.name
@ -167,10 +186,10 @@ def dump_mdense_layer(self, f, hf):
printVector(f, np.transpose(weights[2], (1, 0)), name + '_factor')
activation = self.activation.__name__.upper()
max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2])
f.write('const MDenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}_factor,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
f.write('MDenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}_factor,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))
hf.write('extern const MDenseLayer {};\n\n'.format(name));
hf.write('extern MDenseLayer {};\n\n'.format(name));
return False
MDense.dump_layer = dump_mdense_layer
@ -183,22 +202,22 @@ def dump_conv1d_layer(self, f, hf):
printVector(f, weights[-1], name + '_bias')
activation = self.activation.__name__.upper()
max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0])
f.write('const Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
f.write('Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
hf.write('extern const Conv1DLayer {};\n\n'.format(name));
hf.write('extern Conv1DLayer {};\n\n'.format(name));
return True
Conv1D.dump_layer = dump_conv1d_layer
def dump_embedding_layer_impl(name, weights, f, hf):
printVector(f, weights, name + '_weights')
f.write('const EmbeddingLayer {} = {{\n {}_weights,\n {}, {}\n}};\n\n'
f.write('EmbeddingLayer {} = {{\n {}_weights,\n {}, {}\n}};\n\n'
.format(name, name, weights.shape[0], weights.shape[1]))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
hf.write('extern EmbeddingLayer {};\n\n'.format(name));
def dump_embedding_layer(self, f, hf):
name = self.name

30
src/nnet.h
View File

@ -35,17 +35,17 @@
#define ACTIVATION_SOFTMAX 4
typedef struct {
const float *bias;
const float *input_weights;
float *bias;
float *input_weights;
int nb_inputs;
int nb_neurons;
int activation;
} DenseLayer;
typedef struct {
const float *bias;
const float *input_weights;
const float *factor;
float *bias;
float *input_weights;
float *factor;
int nb_inputs;
int nb_neurons;
int nb_channels;
@ -53,9 +53,9 @@ typedef struct {
} MDenseLayer;
typedef struct {
const float *bias;
const float *input_weights;
const float *recurrent_weights;
float *bias;
float *input_weights;
float *recurrent_weights;
int nb_inputs;
int nb_neurons;
int activation;
@ -63,18 +63,18 @@ typedef struct {
} GRULayer;
typedef struct {
const float *bias;
const float *diag_weights;
const float *recurrent_weights;
const int *idx;
float *bias;
float *diag_weights;
float *recurrent_weights;
int *idx;
int nb_neurons;
int activation;
int reset_after;
} SparseGRULayer;
typedef struct {
const float *bias;
const float *input_weights;
float *bias;
float *input_weights;
int nb_inputs;
int kernel_size;
int nb_neurons;
@ -82,7 +82,7 @@ typedef struct {
} Conv1DLayer;
typedef struct {
const float *embedding_weights;
float *embedding_weights;
int nb_inputs;
int dim;
} EmbeddingLayer;